19 citations · 82 across the 13 of their papers we have counts for
13 papers
USTC-NELSLIP at SemEval-2023 Task 2: Statistical Construction and Dual Adaptation of Gazetteer for Multilingual Complex NER
Jun-Yu Ma, Jia-Chen Gu, Jiajun Qi +3
This paper describes the system developed by the USTC-NELSLIP team for SemEval-2023 Task 2 Multilingual Complex Named Entity Recognition (MultiCoNER II). A method named Statistical…
Multi-Stage Coarse-to-Fine Contrastive Learning for Conversation Intent Induction
Caiyuan Chu, Ya Li, Yifan Liu +4
Intent recognition is critical for task-oriented dialogue systems. However, for emerging domains and new services, it is difficult to accurately identify the key intent of a conver…
Feature Aggregation in Zero-Shot Cross-Lingual Transfer Using Multilingual BERT
Beiduo Chen, Wu Guo, Quan Liu +1
Multilingual BERT (mBERT), a language model pre-trained on large multilingual corpora, has impressive zero-shot cross-lingual transfer capabilities and performs surprisingly well o…
USTC-NELSLIP at SemEval-2022 Task 11: Gazetteer-Adapted Integration Network for Multilingual Complex Named Entity Recognition
Beiduo Chen, Jun-Yu Ma, Jiajun Qi +3
This paper describes the system developed by the USTC-NELSLIP team for SemEval-2022 Task 11 Multilingual Complex Named Entity Recognition (MultiCoNER). We propose a gazetteer-adapt…
Multi-Level Contrastive Learning for Cross-Lingual Alignment
Beiduo Chen, Wu Guo, Bin Gu +2
Cross-language pre-trained models such as multilingual BERT (mBERT) have achieved significant performance in various cross-lingual downstream NLP tasks. This paper proposes a multi…
Detecting Speaker Personas from Conversational Texts
Jia-Chen Gu, Zhen-Hua Ling, Yu Wu +3
Personas are useful for dialogue response prediction. However, the personas used in current studies are pre-defined and hard to obtain before a conversation. To tackle this issue,…
SemEval-2021 Task 4: Reading Comprehension of Abstract Meaning
Boyuan Zheng, Xiaoyu Yang, Yu-Ping Ruan +4
This paper introduces the SemEval-2021 shared task 4: Reading Comprehension of Abstract Meaning (ReCAM). This shared task is designed to help evaluate the ability of machines in re…
DialBERT: A Hierarchical Pre-Trained Model for Conversation Disentanglement
Tianda Li, Jia-Chen Gu, Xiaodan Zhu +4
Disentanglement is a problem in which multiple conversations occur in the same channel simultaneously, and the listener should decide which utterance is part of the conversation he…
Speaker-Aware BERT for Multi-Turn Response Selection in Retrieval-Based Chatbots
Jia-Chen Gu, Tianda Li, Quan Liu +4
In this paper, we study the problem of employing pre-trained language models for multi-turn response selection in retrieval-based chatbots. A new model, named Speaker-Aware BERT (S…
Dually Interactive Matching Network for Personalized Response Selection in Retrieval-Based Chatbots
Jia-Chen Gu, Zhen-Hua Ling, Xiaodan Zhu +1
This paper proposes a dually interactive matching network (DIM) for presenting the personalities of dialogue agents in retrieval-based chatbots. This model develops from the intera…
Condition-Transforming Variational AutoEncoder for Conversation Response Generation
Yu-Ping Ruan, Zhen-Hua Ling, Quan Liu +2
This paper proposes a new model, called condition-transforming variational autoencoder (CTVAE), to improve the performance of conversation response generation using conditional var…
Promoting Diversity for End-to-End Conversation Response Generation
Yu-Ping Ruan, Zhen-Hua Ling, Quan Liu +2
We present our work on Track 2 in the Dialog System Technology Challenges 7 (DSTC7). The DSTC7-Track 2 aims to evaluate the response generation of fully data-driven conversation mo…
Interactive Matching Network for Multi-Turn Response Selection in Retrieval-Based Chatbots
Jia-Chen Gu, Zhen-Hua Ling, Quan Liu
In this paper, we propose an interactive matching network (IMN) for the multi-turn response selection task. First, IMN constructs word representations from three aspects to address…